ABSTRACT
Objective:To investigate the analysis of postoperative death in patients with Stanford B acute aortic dissection (AAD) by XGBoost model.Methods:A retrospective study was conducted on 226 patients with Stanford type B AAD diagnosed in Yunnan Wenshan People′s Hospital from February 2012 to June 2019, including 126 males and 100 females, with an average age of (61.24±4.25) years. According to the outcome of discharge, the patients were divided into survival group ( n=129) and death group ( n=97), in which those who automatically gave up treatment and left the hospital were regarded as the death group. If the patients were admitted to Yunnan Wenshan People′s Hospital for many times during the study period, only the clinical data diagnosed as Stanford B AAD for the first time were selected for the study. The clinical data and hematological indexes of the subjects were collected, and the XGBoost model was used to predict the rapid diagnosis of postoperative death in patients with Stanford B AAD, and compared with the traditional Logistic regression model. Results:In the XGBoost model, the influencing factors were ranked according to the degree of importance. The top 6 factors were hypertension, neutrophil-to-lymphocyte(NLR), C-reactive protein (CRP), white blood cell count(WBC), D-dimer and heart rate. Hypertension and NLR had the greatest influence on postoperative death in patients with Stanford B AAD. Using receiver operator charateristic curve to compare the prediction performance of the two models, it was found that the prediction efficiency of the XGBoost algorithm was significantly stronger than that of the Logistic regression model in the training set, while the two models were equivalent in the verification set. The prediction models constructed by the two methods eventually included independent variables such as hypertension, NLR, CRP, WBC, D-dimer, heart rate, systolic blood pressure, diastolic blood pressure, surgical treatment and so on.Conclusions:XGBoost model can be used to predict the postoperative death of patients with Stanford B AAD. Its diagnostic performance is better than Logistic regression model in training set and equivalent to the latter in verification set. Hypertension and NLR are the most important predictors of postoperative mortality in patients with Stanford B type AAD.
ABSTRACT
Objective To explore the predictive efficacy of XGboost model in predicting risk of relapse and re-admission within 90 d in patients with ischemic stroke,and provide basis for early screening and prevention of high-risk population with ischemic stroke.Methods The clinical data of 6070 primary ischemic stroke patients admitted to our hospital from January 2007 to July 2017 were retrospectively collected.XGboost model and multivariate Logistic regression model were utilized to screen out the influencing factors of relapse and re-admission within 90 d in patients with ischemic stroke.A predictive model was set up.Receiver operating characteristic (ROC) curve was drawn and compared.Sensitivity,specificity and Youden index were calculated and compared to evaluate the prediction performance of XGboost model.Results During the observation period,a total of 520 patients with relapsed ischemic stroke were observed within a period of 90 d,and the incidence density was 8.57％.Multivariate Logistic regression analysis showed that length of first hospital stay,hypertension,pulmonary infection,neutrophil percentage,red blood cell distribution width (variable coefficient),and alkaline phosphatase level were independent influencing factors for re-hospitalization within 90 d of ischemic stroke,(OR=1.016,P=0.000,95％CI:1.008-1.025;OR=4.598,P=0.000,95％CI:3.717-5.687;OR=1.452,P=0.025,95％CI:1.048-2.012;OR=1.013,P=0.006,95％CI:1.004-1.022;OR=1.161,P=0.000,95％CI:1.090-1.237;OR=1.003,P=0.023,95％CI:1.000-1.005).Analysis of importance of risk factors for re-admission of ischemic stroke using XGboost model showed that the top 6 factors were hypertension,red blood cell distribution width,direct bilirubin,length of hospital stay,pulmonary infection,and alkaline phosphatase,and the corresponding importance scores were 32,20,19,18,15 and 14,respectively.ROC curve analysis results indicated that the area under the ROC for re-admission for XGboost model was 0.792 (95％CI:0.717-0.762),which was improved by 5％ as compared with that for multivariate Logistic regression model (0.739 [95％CI:0.764-0.818]).The sensitivity was 89.30％ and the Youden index was 0.444 for XGboost model,which were significantly higher than those for multivariate Logistic regression model (77.3％,0.405).Conclusions XGboost model is superior to multivariate Logistic regression model in predicting recurrence and re-admission of first ischemic stroke patients within 90 d.This model is suitable for prediction and early diagnosis of re-admission of ischemic stroke,which is of great clinical value.